Expectation-confirmation Model Research Articles

Exploring the Relationship Between Avatar Ego Types and Communication: A Comparative Study of South Korea and the United States Based on the Expectation Confirmation Model.

This research explores the significance of avatar communication in the virtual world, where individuals can create new identities and establish relationships beyond real-world limitations. Avatar users engage in virtual interactions to fulfill their desires, enjoy entertainment, and experience surrogate satisfaction. This study integrates the Expectation Confirmation Model (ECM) and Impression Management Theory (IMT) to investigate the impact of various avatar ego types on communication satisfaction and continued intention to use. Two surveys (n = 600) were administered using South Korean and American samples. The results suggest a significant relationship between expectancy confirmation and perceived usefulness. Specifically, high perceived usefulness leads to increased communication satisfaction. Also, when pre-experience expectancy confirmation is low, it positively affects communication satisfaction. In addition, the study highlights differences between Western and Eastern cultural contexts in avatar ego type's expression. This study contributes to the understanding of virtual interactions, offering theoretical insights through the integration of ECM and IMT. Theoretical and practical implications are discussed.

Investigating User Experience of VR Art Exhibitions: The Impact of Immersion, Satisfaction, and Expectation Confirmation

As an innovative form in the digital age, VR art exhibitions have attracted increasing attention. This study aims to explore the key factors that influence visitors’ continuance intention to VR art exhibitions using the expectation confirmation model and experience economy theory and to explore ways to enhance visitor immersion in virtual environments. We conducted a quantitative study of 235 art professionals and enthusiasts, conducted using the partial least squares structural equation modeling (PLS-SEM), to examine the complex relationship between confirmation (CON), Perceived Usefulness (PU), Aesthetic Experiences (AE), Escapist Experiences (EE), Satisfaction (SAT), and Continuance Intention (CI). The results show that confirmation plays a key role in shaping PU, AE, and EE, which in turn positively affect visitors’ SAT. Among these factors, AE positively impacts PU, but EE have no impact. A comprehensive theoretical model was then constructed based on the findings. This research provides empirical support for designing and improving VR art exhibitions. It also sheds light on the application of expectation confirmation theory and experience economy theory in the art field to improve user experience and provides theoretical guidance for the sustainable development of virtual digital art environment.

Antecedents of College Students’ Continuance Behaviors in Online Fragmented Learning: An Empirical Analysis from the Extended ECM Perspective

With the popularity of mobile networks and intelligent terminals, online fragmented learning, as a new learning method, has become the mainstream way for college students to acquire knowledge and study independently. However, college students are prone to “accept-interruption” in online fragmented learning; thus, it is difficult for them to master a complete knowledge system and form a rigorous logic system, which is essential to ensure the effect of online fragmented learning. Therefore, this study investigates the antecedents of college students’ continuance behaviors in online fragmented learning (CBOFL). Based on the expectation confirmation model (ECM), a theoretical model is developed to examine the factors influencing college students’ CBOFL. Taking a total of 429 undergraduate students who have studied contest courses on the Chinese university massive open online courses (MOOCs) for research subjects, the mechanism underlying the determinants of college students’ CBOFL is analyzed, and six hypotheses are tested by a structural equation modeling (SEM) technique with AMOS. The results indicate that confirmation positively impacts intrinsic learning motivation and satisfaction; intrinsic learning motivation, satisfaction, and teachers’ influence all significantly positively affect college students’ CBOFL. Additionally, the predicting powers of different factors on college students’ CBOFL vary broadly; therein, satisfaction has the most significant effect. This study makes theoretical contributions to the quantitative research on college students’ CBOFL and literature on the ECM. Still, it also has important practical significance in guiding college students’ CBOFL and facilitating the sustainability of online fragmented learning.

Users' continuance intention towards an AI painting application: An extended expectation confirmation model.

With the rapid advancement of technology, Artificial Intelligence (AI) painting has emerged as a leading intelligence service. This study aims to empirically investigate users' continuance intention toward AI painting applications by utilizing and expanding the Expectation Confirmation Model (ECM), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and the Flow Theory. A comprehensive research model is proposed. A total of 443 questionnaires were distributed to users with AI painting experiences for data collection. The hypotheses were tested through structural equation modeling. The primary conclusions drawn from this research include: 1) Confirmation plays a crucial role, significantly and positively predicting satisfaction and social impact. 2) Personal innovativeness has a significant effect on confirmation. 3) Satisfaction, flow experience, and social influence directly and positively predict intention, with social influence showing the most significant impact, while perceived usefulness, perceived enjoyment, and performance expectancy show no significant impact on intention. 4) Habit plays a negative moderating role in the association between social influence and continued intention to use. These findings offer valuable insights and inspiration for users seeking to understand the appropriate utilization of AI painting and provide actionable directions for the development of AI painting.

Factors influencing continuance intention to use Instagram among Wrocław University students

In the digital era, social networking sites have redefined how people connect worldwide. These platforms enable unprecedented global connections among friends, family, and even strangers, fostering a shared sense of community. This study focuses on Instagram, a pivotal platform launched in 2010, that allows users to share photos and videos enhanced by filters and editing tools. Instagram offers features like Stories, IGTV and Reels, nurturing creativity, fostering connections, and deepening the cultural impact. The research aims to analyse factors influencing individuals’ intentions to continue using Instagram. Employing the Expectation-Confirmation Model alongside variables such as sense of belonging, social interaction, passing time, information seeking, and self-expression, the study employed a survey method to figure out which variables determined the continued intention to use social networks among Wrocław University students. Data from 120 respondents were analysed, revealing support for nine out of ten hypotheses. Results revealed that a sense of belonging exerts the strongest influence on continued intent, followed by perceived usefulness, satisfaction, self-expression, information seeking, and social interaction. Notably, the passing time did not have a significant effect on continuance intention to use Instagram.

Older Adults' Continuance Intentions for Online Physical Exercise Classes.

During the COVID-19 pandemic, lockdowns and social distancing measures drove the shift from in-person to online physical exercise classes, leading individuals to explore these digital alternatives. Guided by the Expectation-Confirmation Model, this article examines older adults' intentions to continue using online physical exercise classes. Semi-structured interviews were conducted with 17 adults aged 65 and older who had participated in online physical exercise classes during the pandemic. Transcripts were subject to thematic analysis using the NVivo software program. The results indicate that older adults recognize the usefulness of online physical exercise classes because of their ability to enhance their health and well-being. Their initial expectations were surpassed, and they were generally satisfied with the experience. However, in-person classes remained preferred due to their enhanced benefits. They also felt that the adoption of online classes was involuntary; instead of an autonomous decision guided by their needs and preferences, this was a viable solution imposed by the lockdown. Therefore, their continuance intentions are limited to specific conditions, namely a new lockdown or other physical impediments. Still, considering the flexibility that online physical exercise classes offer, accommodating time and physical constraints, participants highlighted the advantages of a hybrid approach for those who may face challenges attending in-person classes. Based on the findings, this article proposes that ECM provides a relevant, yet insufficient, framework for explaining older adults' continuance intentions for online physical exercise classes, suggesting the inclusion of additional explaining factors: perceived usefulness of non-technological alternatives, necessary conditions, and self-determination.

The impact of usability factors on continuance intention to use the system for acquisition and evaluation of digital competences in the domain of education

Built on a stream of literature that has identified a lack of studies that examine the post-adoption behavior of information systems (IS) users, and especially, in relation to the usability of systems that support teaching and learning processes, this paper proposed an usability-extended Expectation-Confirmation Model (ECM) to determine the variables that impact users' continuance intention to use the system for acquisition and evaluation of digital competence (DC) – the CRISS platform. The model was tested using a cross-sectional survey instrument on 353 primary and secondary school teachers in six European countries who participated in the ongoing pilot project and used the CRISS platform. Findings provided solid support for theoretical relationships posited in the original ECM. Furthermore, the continuance intention of the CRISS platform in the model was directly driven by teachers’ levels of satisfaction, perceived usefulness and perceived efficiency, and indirectly through satisfaction by perceived effectiveness. However, the level of computer skill and duration of use, expressed in hours per week, did not moderate the satisfaction-continuance intention relationship.

Determinants of an auditor’s continuance intention with respect to use of the Audit Tools and Linked Archives System (ATLAS): A model of extended expectation confirmation

The aim of this study was to examine the determinants of the continuance intention with respect to use of the Audit Tools and Linked Archives System (ATLAS) by employing survey methods. These determinants are developed from an Expectation Confirmation Model (ECM). The sample of this study is auditors who use ATLAS in public accounting firms in Indonesia. As many as 356 data points can be processed using smartPLS. This study revealed that perceived usefulness, confirmation, information quality, top management commitment and satisfaction affected the auditor’s intentions when using ATLAS. The implications of this study are (1) Public accounting firms must provide full support to auditors in using ATLAS and equip auditors through training so auditors understand that using ATLAS is very useful; (2) IAPI must pay attention to outputs that are complete, good and appropriate so that the auditor is satisfied when using ATLAS. The auditor has a tendency to continue using ATLAS if he is satisfied.

Determinants of Mobile Shopping Apps Continuance Intention: A Review and Proposed Conceptual Model

Mobile shopping apps have experienced significant global growth due to the widespread adoption of mobile devices in everyday life. The exponential expansion of mobile shopping app usage has brought up both opportunities and challenges for service providers. For online businesses, maintaining their current users is a vital means of gaining a competitive edge. Hence, the primary objective of this research is to develop a conceptual model to examine the factors that influence users' intention to continue using mobile shopping apps. The Expectation-Confirmation Model (ECM) and the Theory of Network Externalities offer initial foundations for developing a unified conceptual model. This research has the potential to make a substantial contribution to the understanding of factors that influence continuance intention to use mobile shopping apps. It can benefit academicians, online businesses, service providers and mobile marketing practitioners

FACTORS MOTIVATING STUDENTS TO CONTINUE USING YOUTUBE AS A LEARNING AID POST-COVID-19 PANDEMIC

During the COVID-19 pandemic, the process of teaching and learning had to be conducted online, such as through Zoom meetings, due to physical meeting restrictions, including in higher education. However, this was not entirely effective due to various factors, such as individual student learning preferences. To enhance comprehension, students utilized video-sharing platforms like YouTube, which, as it turns out, was effective. Hence, this study aims to investigate what factors motivate students (both school and university students) to continue using YouTube as a learning medium post-COVID-19 pandemic. A theoretical lens of the Expectation Confirmation Model is used with the addition of external variables: Perceived Herd and Parasocial Interaction. A total of 242 respondents from both educational levels in Manokwari Regency were obtained and analyzed using Partial Least Square - Structural Equation Modelling (PLS-SEM). The study's results explain that students experience satisfaction in using YouTube, which motivates them in Manokwari to continue using it as a learning medium post-COVID-19. This satisfaction is influenced by variables such as confirmation (C), perceived usefulness (PU), and perceived herd (PH). However, the PU factor's impact on continuance usage (CU) has a t-statistic value <1.96 and a p-value >0.05, indicating it lacks significant influence. There is also a rejection of the parasocial interaction (PI) variable's influence on satisfaction (S). Furthermore, the use of control variables such as age and gender has shown significant influence on this study, with research findings indicating that age and gender significantly impact satisfaction and continuance usage.
 
 Keywords: Control Variables, COVID-19, Expectation Confirmation Model, Parasocial Interaction, Perceived Herd, YouTube

Exploring the Factors Influencing Continuance Intention to Use AI Drawing Tools: Insights from Designers

With the continuous evolution of artificial intelligence technology, AI drawing tools have emerged as highly esteemed instruments in the modern design industry. These tools, owing to their exceptional performance and innovative features, offer creators an unprecedented artistic experience. However, the factors influencing designers’ continuance intention to use AI drawing tools remain ambiguous. This study is grounded in the expectation–confirmation model–information systems continuance (ECM-ISC) model, which is further refined and hypothesized in light of the characteristics of AI drawing tools. Using structural equation modeling, we analyzed 398 valid questionnaire responses. The results elucidated the relationships of key constructs, such as perceived usefulness, perceived ease of use, satisfaction, expectation confirmation, perceived playfulness, perceived switching cost, subjective norms, and perceived risk, on designers’ continuance intention. Notably, perceived ease of use, traditionally considered vital, did not result in a significant influence on continuance intention or perceived usefulness in this research. This insight offers new perspectives for AI drawing tool developers and designers, suggesting that while pursuing user friendliness, broader considerations affecting user decisions should be taken into account. This study not only enriches the theoretical framework but also provides valuable guidance for the practical field.

The Impact of Aesthetic Expectations and Aesthetic Experiential Qualities on Tourist Satisfaction: A Case Study of the Zhangjiajie National Forest Park

Aesthetic expectations often constitute the primary focus in marketing nature-based tourist destinations. However, academic research has insufficiently explored the disparity between tourists’ aesthetic expectations and the actual aesthetic quality maintenance in shaping satisfaction. Employing the Expectation Confirmation Theory, this study utilized structural equation modeling techniques to analyze survey data (n = 446). It proposed and tested an Aesthetic Expectation Confirmation Model to examine the relationship between aesthetic expectations, experiential qualities, and tourist satisfaction in the Zhangjiajie National Forest Park. The empirical findings show that aesthetic expectations have a direct, negative impact on satisfaction, while aesthetic expectation confirmation has a positive direct impact on satisfaction. Moreover, aesthetic expectation confirmation also plays a mediating role in the influence of aesthetic expectations and experiential quality on satisfaction. Specifically, aesthetic expectations indirectly impact satisfaction negatively through aesthetic expectation confirmation, whereas aesthetic experiential qualities have a positive, indirect impact on satisfaction through the same process. These findings offer theoretical contributions to the literature on forest recreation aesthetics and hold practical significance for the planning and management of destination aesthetics.

Factors influencing continuance intention to use mobile banking: an extended expectation-confirmation model with moderating role of trust

This study explores the impacts of user adaptation and its antecedents, including perceived usefulness, self-efficacy, and subjective norm on continuance intention towards mobile banking. Based on (1) extended expectation-confirmation model, (2) adaptive structuration theory for individuals, and (3) decomposed theory of planned behavior, and by incorporating user adaptation and trust perspectives, this study proposes and validates a research model by using convenient and snowball sampling techniques, face-to-face survey with a sample of 523 consumers of large banks in Vietnam. The partial least squares structural equation modeling SmartPLS 4.0 software is employed for data analysis. The research findings reveal that (1) except for subjective norm, perceived usefulness, satisfaction, adaptation, and self-efficacy significantly impact continuance intention; (2) trust moderates the relationship between adaptation and continuance intention. Theoretical and managerial implications are provided to broaden the information technology continuance literature, suggesting how banks should sustain strong relationships with mobile banking customers.

AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students-an integrated analysis utilizing UTAUT and ECM models.

This study is centered on investigating the acceptance and utilization of AI Chatbot technology among graduate students in China and its implications for higher education. Employing a fusion of the UTAUT (Unified Theory of Acceptance and Use of Technology) model and the ECM (Expectation-Confirmation Model), the research seeks to pinpoint the pivotal factors influencing students' attitudes, satisfaction, and behavioral intentions regarding AI Chatbots. The study constructs a model comprising seven substantial predictors aimed at precisely foreseeing users' intentions and behavior with AI Chatbots. Collected from 373 students enrolled in various universities across China, the self-reported data is subject to analysis using the partial-least squares method of structural equation modeling to confirm the model's reliability and validity. The findings validate seven out of the eleven proposed hypotheses, underscoring the influential role of ECM constructs, particularly "Confirmation" and "Satisfaction," outweighing the impact of UTAUT constructs on users' behavior. Specifically, users' perceived confirmation significantly influences their satisfaction and subsequent intention to continue using AI Chatbots. Additionally, "Personal innovativeness" emerges as a critical determinant shaping users' behavioral intention. This research emphasizes the need for further exploration of AI tool adoption in educational settings and encourages continued investigation of their potential in teaching and learning environments.

Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis

The influence of COVID-19 on educational processes has halted physical forms of teaching and learning and initiated online and mobile learning systems in most countries. The provision and usage of online and e-learning systems are becoming the main challenge for many universities during the COVID-19 pandemic. Due to the novelty of this situation, a substantial amount of research has been carried out to investigate the issue of m-learning adoption or acceptance. Nevertheless, little is known about studying to examine the continued use of m-learning, which is still in short supply and calls for further research. Five different theoretical models are integrated into this study to develop an integrated model that overcomes this limitation, including the technology acceptance model, the theory of planned behavior, the expectation-confirmation model, the Delone and McLean Information System Success Model, and the Unified Theory of Acceptance and Utilization of Technology 2. This conceptual framework shows novel relationships between variables by integrating trust, personal innovation, learning value, instructor quality, and course quality. Unlike extant literature, this study utilized a hybrid analysis methodology combining two-stage analysis using partial least squares structural equation modeling (PLS-SEM) and evolving artificial intelligence named deep learning (Artificial Neural Network [ANN]) on 250 usable responses. The sensitivity analysis results revealed that attitude has the most considerable effect on the continued use of m-learning, with 100% normalized importance, followed by perceived usefulness (88%), satisfaction (77%), and habit (61%). This research reveals that a “deep ANN architecture” may determine the non-linear relationships between variables in the theoretical model. Further theoretical and practical implications are also discussed.

Exploring students’ acceptance and continuance intention in using immersive virtual reality and metaverse integrated learning environments: The case of an Italian university course

AbstractImmersive virtual reality (IVR) and Metaverse applications are gaining attention in the educational field, showing potentials in transforming traditional learning methods by supporting active and experiential forms of learning. Our study, conducted within the context of an Italian university course, employs the Extended Expectation-Confirmation Model (EECM) as a theoretical framework to explore the key aspects of students’ acceptance and continued intention to use IVR and Metaverse integrated learning environments in educational settings. The EECM, which bridges the gap between pre-adoption expectations and post-adoption experiences, provides a comprehensive perspective for exploring technology adoption in education. Students’ attitudes were assessed before and after they completed an elective course offered by the university that delved into IVR and Metaverse applications. During the course, students explored the theoretical and practical applications of these technologies, engaging in a variety of experiences, from immersive relaxation exercises to immersive educational platforms in the emerging Metaverse. Contrary to common assumptions, pre-adoption factors like performance and effort expectancy had limited impact on expectancy confirmation. However, when students’ initial expectations matched their experiences, their perceptions of the technology’s usefulness, satisfaction, and confidence in its use were positively enhanced, influencing their continued intention to integrate these tools in education.

Understanding the Motivations for Continuance Usage of Mobile Apps

ABSTRACT The evolution of mobile technology has contributed to the growth seen in mobile applications (m-apps) usage. Since businesses have accelerated their development of mobile platforms for their services, the number of m-apps that are available to users has increased. This poses a challenge for companies, as they seek to retain a long-term user base they also need to be aligned with consumers’ beliefs and wants, and promote the use of their own m-apps. This research investigates the motivations behind users’ intention to continuously use m-apps. We employ the expectation-confirmation model (ECM) and incorporate the perspective of lifestyle compatibility. A total of 175 responses to questionnaires were obtained from an online survey to empirically validate the proposed model, using the partial least squares (PLS) method. The findings demonstrate that lifestyle compatibility is a strong predictor for continuance intention of m-apps.

Academics' continuance intention to use learning technologies during COVID-19 and beyond.

The COVID-19 pandemic has revolutionized the teaching pedagogy in higher education as universities are forecasted to increase investments in learning technology infrastructure to transition away from traditional teaching methods. Therefore, it is crucial to investigate whether academics intend to continually integrate learning technologies as part of a permanent pedagogical change beyond the COVID-19 pandemic. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT), and Expectation Confirmation Model (ECM), this study examines the salient determinants influencing the continuance intention of academics to use learning technologies in their teaching pedagogy during and after COVID-19. Primary data collected from a private university was analyzed using the partial least squares structural equation modelling technique (PLS-SEM). The findings revealed two sequential mediating relationships which serve as the mechanism linking the relationship between facilitating conditions and their continuance intention to use learning technologies during and beyond the COVID-19 pandemic.

Continued Usage Intention of Mobile Learning (M-Learning) in Iraqi Universities Under an Unstable Environment: Integrating the ECM and UTAUT2 Models

Aim/Purpose: This study examines the adoption and continued use of m-learning in Iraqi universities amidst an unstable environment by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Expectation-Confirmation Model (ECM) models. The primary goal is to address the specific challenges and opportunities in Iraq’s higher education institutions (HEIs) due to geopolitical instability and understand their impact on student acceptance, satisfaction, and continued m-learning usage. Background: The research builds on the growing importance of m-learning, especially in HEIs, and recognizes the unique challenges faced by institutions in Iraq, given the region’s instability. It identifies gaps in existing models and proposes extensions, introducing the variable “civil conflicts” to account for the volatile context. The study aims to contribute to a deeper understanding of m-learning acceptance in conflict-affected regions and provide insights for improving m-learning initiatives in Iraqi HEIs. Methodology: To achieve its objectives, this research employed a quantitative survey to collect data from 399 students in five Iraqi universities. PLS-SEM is used for the analysis of quantitative data, testing the extended UTAUT2 and ECM models. Contribution: The study’s findings are expected to contribute to the development of a nuanced understanding of m-learning adoption and continued usage in conflict-affected regions, particularly in the Iraqi HEI context. Findings: The study’s findings may inform strategies to enhance the effectiveness of m-learning initiatives in Iraqi HEIs and offer insights into how education can be supported in regions characterized by instability. Recommendations for Practitioners: Educators and policymakers can benefit from the research by making informed decisions to support education continuity and quality, particularly in conflict-affected areas. Recommendation for Researchers: Researchers can build upon this study by further exploring the adoption and usage of m-learning in unstable environments and evaluating the effectiveness of the proposed model extensions. Impact on Society: The research has the potential to positively impact society by improving access to quality education in regions affected by conflict and instability. Future Research: Future research can expand upon this study by examining the extended model’s applicability in different conflict-affected regions and assessing the long-term impact of m-learning initiatives on students’ educational outcomes.

Factors Impact on Fine Art Major Student’s Continuance Intention to Blended Education of the Public Universities in Chengdu Region

A blended learning strategy combines traditional in-person instruction with online education. During the COVID-19 pandemic, traditional face-to-face teaching has been severely challenged. Blended learning, which combines the advantages of traditional learning methods and online learning, has gradually been widely adopted by the majority universities in China. The objective of this quantitative research is to investigate the elements that influence students majoring in painting at Chengdu’s public colleges and universities’ continuance intention to engage in blended learning. The information systems success model, the expectation confirmation model, and the theory of planned behavior provided the intellectual foundation of this research. It is assumed that the five latent variables of information quality, system quality, perceived usefulness, satisfaction, and attitude have the significant impact on continuation intention. The study adopted quantitative research, selected three public colleges and universities in the Chengdu area, and stratified sampling students who had experience in blended learning, obtained valid questionnaires, and explored the key factors affecting college students’ continuous use of blended education in the post-epidemic era. Based on the conclusion of this study: the attitude of the target students had the greatest impact on continuance intention. These factors also provide guidelines for schools to implement blended learning in teaching and learning environments.